Sharing a whole-/total-body [18F]FDG-PET/CT dataset with CT-derived segmentations: an ENHANCE.PET initiative
- Daria Ferrara
- Manuel Pires
- Sebastian Gutschmayer
- Josef Yu
- Yasser G. Abdelhafez
- Elisabetta Abenavoli
- Ramsey D. Badawi
- Abhijit J. Chaudhari
- Moon S. Chen
- Simon R. Cherry
- Armin Frille
- Barbara K. Geist
- Stefan Gruenert
- Marcus Hacker
- Swen Hesse
- Teresa Kerkhoff
- Pia Linder
- Johanna Pappisch
- Smilla Pusitz
- Osama A. Raslan
- Ivo Rausch
- Siba P. Raychaudhuri
- Osama Sabri
- Fabian P. Schmidt
- Roberto Sciagrà
- Benjamin A. Spencer
- Guobao Wang
- Hubert Wirtz
- Thomas Beyer
- Lalith Kumar Shiyam Sundar
2026-04-14
We present a large whole-body and total-body curated dataset of dual-modality 2-deoxy-2-[ 18 F]fluoro-D-glucose (FDG)-Positron Emission Tomography/Computed Tomography (PET/CT) studies, consisting of 1,683 PET/CT images and the corresponding CT-derived segmentations of 130 target regions. This multi-center dataset includes images from individuals without overt disease and patients with a range of malignant and inflammatory pathologies, including arthritis, lymphoma, and melanoma, as well as cancers of the lung, head-neck, and genito-urinary tract. Target regions were first automatically segmented from CT images using an in-house software and subsequently verified and corrected by physicians-in-training. In total, the segmented regions encompass 130 volumes, including abdominal organs, muscles, bones, cardiac subregions, vessels, adipose tissue, and skeletal muscle around the third lumbar vertebra. PET/CT images and corresponding CT-derived segmentations are provided in anonymized NIfTI format. The dataset can be used for deep learning training, validation, or multi-modality image analysis and thus fills an important gap in available resources to advance the use of PET/CT data in clinical management.